Overview

Dataset statistics

Number of variables14
Number of observations610
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory62.7 KiB
Average record size in memory105.2 B

Variable types

Numeric9
Categorical4
Boolean1

Alerts

season has constant value ""Constant
instant is highly overall correlated with dteday and 1 other fieldsHigh correlation
temp is highly overall correlated with atempHigh correlation
atemp is highly overall correlated with tempHigh correlation
casual is highly overall correlated with registered and 1 other fieldsHigh correlation
registered is highly overall correlated with casual and 1 other fieldsHigh correlation
cnt is highly overall correlated with casual and 1 other fieldsHigh correlation
dteday is highly overall correlated with instant and 2 other fieldsHigh correlation
holiday is highly overall correlated with instant and 1 other fieldsHigh correlation
weekday is highly overall correlated with dtedayHigh correlation
holiday is highly imbalanced (76.1%)Imbalance
instant is uniformly distributedUniform
instant has unique valuesUnique
hr has 26 (4.3%) zerosZeros
windspeed has 62 (10.2%) zerosZeros
casual has 172 (28.2%) zerosZeros

Reproduction

Analysis started2023-10-19 13:44:37.467514
Analysis finished2023-10-19 13:44:53.055272
Duration15.59 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

instant
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct610
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean305.5
Minimum1
Maximum610
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-10-19T19:14:53.190685image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile31.45
Q1153.25
median305.5
Q3457.75
95-th percentile579.55
Maximum610
Range609
Interquartile range (IQR)304.5

Descriptive statistics

Standard deviation176.23611
Coefficient of variation (CV)0.57687761
Kurtosis-1.2
Mean305.5
Median Absolute Deviation (MAD)152.5
Skewness0
Sum186355
Variance31059.167
MonotonicityStrictly increasing
2023-10-19T19:14:53.348456image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
410 1
 
0.2%
403 1
 
0.2%
404 1
 
0.2%
405 1
 
0.2%
406 1
 
0.2%
407 1
 
0.2%
408 1
 
0.2%
409 1
 
0.2%
411 1
 
0.2%
Other values (600) 600
98.4%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
610 1
0.2%
609 1
0.2%
608 1
0.2%
607 1
0.2%
606 1
0.2%
605 1
0.2%
604 1
0.2%
603 1
0.2%
602 1
0.2%
601 1
0.2%

dteday
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
01-01-2011
 
24
21-01-2011
 
24
20-01-2011
 
24
17-01-2011
 
24
16-01-2011
 
24
Other values (23)
490 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters6100
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01-01-2011
2nd row01-01-2011
3rd row01-01-2011
4th row01-01-2011
5th row01-01-2011

Common Values

ValueCountFrequency (%)
01-01-2011 24
 
3.9%
21-01-2011 24
 
3.9%
20-01-2011 24
 
3.9%
17-01-2011 24
 
3.9%
16-01-2011 24
 
3.9%
13-01-2011 24
 
3.9%
10-01-2011 24
 
3.9%
15-01-2011 24
 
3.9%
08-01-2011 24
 
3.9%
09-01-2011 24
 
3.9%
Other values (18) 370
60.7%

Length

2023-10-19T19:14:53.517753image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01-01-2011 24
 
3.9%
20-01-2011 24
 
3.9%
17-01-2011 24
 
3.9%
16-01-2011 24
 
3.9%
13-01-2011 24
 
3.9%
10-01-2011 24
 
3.9%
15-01-2011 24
 
3.9%
08-01-2011 24
 
3.9%
09-01-2011 24
 
3.9%
21-01-2011 24
 
3.9%
Other values (18) 370
60.7%

Most occurring characters

ValueCountFrequency (%)
1 2122
34.8%
0 1477
24.2%
- 1220
20.0%
2 857
14.0%
5 70
 
1.1%
3 69
 
1.1%
4 69
 
1.1%
6 63
 
1.0%
7 55
 
0.9%
8 51
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4880
80.0%
Dash Punctuation 1220
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2122
43.5%
0 1477
30.3%
2 857
17.6%
5 70
 
1.4%
3 69
 
1.4%
4 69
 
1.4%
6 63
 
1.3%
7 55
 
1.1%
8 51
 
1.0%
9 47
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 1220
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2122
34.8%
0 1477
24.2%
- 1220
20.0%
2 857
14.0%
5 70
 
1.1%
3 69
 
1.1%
4 69
 
1.1%
6 63
 
1.0%
7 55
 
0.9%
8 51
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2122
34.8%
0 1477
24.2%
- 1220
20.0%
2 857
14.0%
5 70
 
1.1%
3 69
 
1.1%
4 69
 
1.1%
6 63
 
1.0%
7 55
 
0.9%
8 51
 
0.8%

season
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
Spring
610 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters3660
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSpring
2nd rowSpring
3rd rowSpring
4th rowSpring
5th rowSpring

Common Values

ValueCountFrequency (%)
Spring 610
100.0%

Length

2023-10-19T19:14:53.656050image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-19T19:14:53.835399image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
spring 610
100.0%

Most occurring characters

ValueCountFrequency (%)
S 610
16.7%
p 610
16.7%
r 610
16.7%
i 610
16.7%
n 610
16.7%
g 610
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3050
83.3%
Uppercase Letter 610
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 610
20.0%
r 610
20.0%
i 610
20.0%
n 610
20.0%
g 610
20.0%
Uppercase Letter
ValueCountFrequency (%)
S 610
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3660
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 610
16.7%
p 610
16.7%
r 610
16.7%
i 610
16.7%
n 610
16.7%
g 610
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 610
16.7%
p 610
16.7%
r 610
16.7%
i 610
16.7%
n 610
16.7%
g 610
16.7%

hr
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.795082
Minimum0
Maximum23
Zeros26
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-10-19T19:14:53.944407image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median12
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.8521071
Coefficient of variation (CV)0.58092916
Kurtosis-1.1454554
Mean11.795082
Median Absolute Deviation (MAD)6
Skewness-0.072058919
Sum7195
Variance46.951372
MonotonicityNot monotonic
2023-10-19T19:14:54.105716image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
12 27
 
4.4%
17 27
 
4.4%
16 27
 
4.4%
15 27
 
4.4%
14 27
 
4.4%
13 27
 
4.4%
1 26
 
4.3%
22 26
 
4.3%
21 26
 
4.3%
20 26
 
4.3%
Other values (14) 344
56.4%
ValueCountFrequency (%)
0 26
4.3%
1 26
4.3%
2 24
3.9%
3 16
2.6%
4 20
3.3%
5 24
3.9%
6 26
4.3%
7 26
4.3%
8 26
4.3%
9 26
4.3%
ValueCountFrequency (%)
23 26
4.3%
22 26
4.3%
21 26
4.3%
20 26
4.3%
19 26
4.3%
18 26
4.3%
17 27
4.4%
16 27
4.4%
15 27
4.4%
14 27
4.4%

holiday
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size738.0 B
False
586 
True
 
24
ValueCountFrequency (%)
False 586
96.1%
True 24
 
3.9%
2023-10-19T19:14:54.304565image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

weekday
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
Saturday
95 
Sunday
94 
Monady
93 
Friday
85 
Wednesday
84 
Other values (2)
159 

Length

Max length9
Median length8
Mean length7.1147541
Min length6

Characters and Unicode

Total characters4340
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSaturday
2nd rowSaturday
3rd rowSaturday
4th rowSaturday
5th rowSaturday

Common Values

ValueCountFrequency (%)
Saturday 95
15.6%
Sunday 94
15.4%
Monady 93
15.2%
Friday 85
13.9%
Wednesday 84
13.8%
Tuesday 80
13.1%
Thursday 79
13.0%

Length

2023-10-19T19:14:54.452788image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-19T19:14:54.690930image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
saturday 95
15.6%
sunday 94
15.4%
monady 93
15.2%
friday 85
13.9%
wednesday 84
13.8%
tuesday 80
13.1%
thursday 79
13.0%

Most occurring characters

ValueCountFrequency (%)
a 705
16.2%
d 694
16.0%
y 610
14.1%
u 348
8.0%
n 271
 
6.2%
r 259
 
6.0%
e 248
 
5.7%
s 243
 
5.6%
S 189
 
4.4%
T 159
 
3.7%
Other values (7) 614
14.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3730
85.9%
Uppercase Letter 610
 
14.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 705
18.9%
d 694
18.6%
y 610
16.4%
u 348
9.3%
n 271
 
7.3%
r 259
 
6.9%
e 248
 
6.6%
s 243
 
6.5%
t 95
 
2.5%
o 93
 
2.5%
Other values (2) 164
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
S 189
31.0%
T 159
26.1%
M 93
15.2%
F 85
13.9%
W 84
13.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 4340
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 705
16.2%
d 694
16.0%
y 610
14.1%
u 348
8.0%
n 271
 
6.2%
r 259
 
6.0%
e 248
 
5.7%
s 243
 
5.6%
S 189
 
4.4%
T 159
 
3.7%
Other values (7) 614
14.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4340
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 705
16.2%
d 694
16.0%
y 610
14.1%
u 348
8.0%
n 271
 
6.2%
r 259
 
6.0%
e 248
 
5.7%
s 243
 
5.6%
S 189
 
4.4%
T 159
 
3.7%
Other values (7) 614
14.1%

weathersit
Categorical

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
1
368 
2
194 
3
47 
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters610
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 368
60.3%
2 194
31.8%
3 47
 
7.7%
4 1
 
0.2%

Length

2023-10-19T19:14:54.875916image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-19T19:14:55.061759image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 368
60.3%
2 194
31.8%
3 47
 
7.7%
4 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 368
60.3%
2 194
31.8%
3 47
 
7.7%
4 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 610
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 368
60.3%
2 194
31.8%
3 47
 
7.7%
4 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 610
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 368
60.3%
2 194
31.8%
3 47
 
7.7%
4 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 610
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 368
60.3%
2 194
31.8%
3 47
 
7.7%
4 1
 
0.2%

temp
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19688525
Minimum0.02
Maximum0.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-10-19T19:14:55.221818image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.06
Q10.16
median0.2
Q30.235
95-th percentile0.36
Maximum0.46
Range0.44
Interquartile range (IQR)0.075

Descriptive statistics

Standard deviation0.081303845
Coefficient of variation (CV)0.41295042
Kurtosis1.2601325
Mean0.19688525
Median Absolute Deviation (MAD)0.04
Skewness0.66508679
Sum120.1
Variance0.0066103152
MonotonicityNot monotonic
2023-10-19T19:14:55.412701image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.2 89
14.6%
0.16 84
13.8%
0.22 79
13.0%
0.14 56
9.2%
0.18 54
8.9%
0.24 40
 
6.6%
0.26 32
 
5.2%
0.12 29
 
4.8%
0.1 22
 
3.6%
0.3 19
 
3.1%
Other values (13) 106
17.4%
ValueCountFrequency (%)
0.02 10
 
1.6%
0.04 14
 
2.3%
0.06 11
 
1.8%
0.08 9
 
1.5%
0.1 22
 
3.6%
0.12 29
 
4.8%
0.14 56
9.2%
0.16 84
13.8%
0.18 54
8.9%
0.2 89
14.6%
ValueCountFrequency (%)
0.46 6
 
1.0%
0.44 3
 
0.5%
0.42 6
 
1.0%
0.4 8
1.3%
0.38 3
 
0.5%
0.36 8
1.3%
0.34 5
 
0.8%
0.32 13
2.1%
0.3 19
3.1%
0.28 10
1.6%

atemp
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20201754
Minimum0
Maximum0.4545
Zeros2
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-10-19T19:14:55.577717image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0758
Q10.1515
median0.197
Q30.2424
95-th percentile0.3485
Maximum0.4545
Range0.4545
Interquartile range (IQR)0.0909

Descriptive statistics

Standard deviation0.080569827
Coefficient of variation (CV)0.39882589
Kurtosis0.99018463
Mean0.20201754
Median Absolute Deviation (MAD)0.0455
Skewness0.59206949
Sum123.2307
Variance0.0064914969
MonotonicityNot monotonic
2023-10-19T19:14:55.734420image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.197 70
11.5%
0.2273 64
 
10.5%
0.2121 57
 
9.3%
0.1818 47
 
7.7%
0.1515 45
 
7.4%
0.1212 41
 
6.7%
0.1364 39
 
6.4%
0.2576 38
 
6.2%
0.1667 25
 
4.1%
0.2727 23
 
3.8%
Other values (19) 161
26.4%
ValueCountFrequency (%)
0 2
 
0.3%
0.0152 2
 
0.3%
0.0303 7
 
1.1%
0.0455 2
 
0.3%
0.0606 10
 
1.6%
0.0758 15
 
2.5%
0.0909 7
 
1.1%
0.1061 17
2.8%
0.1212 41
6.7%
0.1364 39
6.4%
ValueCountFrequency (%)
0.4545 5
 
0.8%
0.4394 2
 
0.3%
0.4242 7
1.1%
0.4091 10
1.6%
0.3939 2
 
0.3%
0.3485 6
 
1.0%
0.3333 12
2.0%
0.3182 8
1.3%
0.303 10
1.6%
0.2879 17
2.8%

hum
Real number (ℝ)

Distinct61
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56247541
Minimum0.21
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-10-19T19:14:55.929203image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.21
5-th percentile0.33
Q10.4325
median0.52
Q30.69
95-th percentile0.93
Maximum1
Range0.79
Interquartile range (IQR)0.2575

Descriptive statistics

Standard deviation0.17544016
Coefficient of variation (CV)0.31190725
Kurtosis-0.48216689
Mean0.56247541
Median Absolute Deviation (MAD)0.11
Skewness0.60955293
Sum343.11
Variance0.030779248
MonotonicityNot monotonic
2023-10-19T19:14:56.131583image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.47 37
 
6.1%
0.93 32
 
5.2%
0.55 31
 
5.1%
0.5 28
 
4.6%
0.59 27
 
4.4%
0.51 23
 
3.8%
0.69 23
 
3.8%
0.4 19
 
3.1%
0.86 18
 
3.0%
0.41 17
 
2.8%
Other values (51) 355
58.2%
ValueCountFrequency (%)
0.21 1
 
0.2%
0.25 1
 
0.2%
0.26 4
0.7%
0.27 1
 
0.2%
0.28 9
1.5%
0.29 1
 
0.2%
0.3 9
1.5%
0.32 4
0.7%
0.33 5
0.8%
0.34 3
 
0.5%
ValueCountFrequency (%)
1 1
 
0.2%
0.94 4
 
0.7%
0.93 32
5.2%
0.92 1
 
0.2%
0.88 4
 
0.7%
0.87 10
 
1.6%
0.86 18
3.0%
0.82 3
 
0.5%
0.81 5
 
0.8%
0.8 15
2.5%

windspeed
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20485098
Minimum0
Maximum0.5821
Zeros62
Zeros (%)10.2%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-10-19T19:14:56.329280image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1045
median0.194
Q30.2836
95-th percentile0.4179
Maximum0.5821
Range0.5821
Interquartile range (IQR)0.1791

Descriptive statistics

Standard deviation0.12180591
Coefficient of variation (CV)0.59460739
Kurtosis-0.11080241
Mean0.20485098
Median Absolute Deviation (MAD)0.0895
Skewness0.33902938
Sum124.9591
Variance0.01483668
MonotonicityNot monotonic
2023-10-19T19:14:56.468976image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.1642 65
10.7%
0 62
10.2%
0.194 54
8.9%
0.1343 54
8.9%
0.1045 51
8.4%
0.2537 49
8.0%
0.2836 47
7.7%
0.2239 45
7.4%
0.0896 45
7.4%
0.3284 34
 
5.6%
Other values (10) 104
17.0%
ValueCountFrequency (%)
0 62
10.2%
0.0896 45
7.4%
0.1045 51
8.4%
0.1343 54
8.9%
0.1642 65
10.7%
0.194 54
8.9%
0.2239 45
7.4%
0.2537 49
8.0%
0.2836 47
7.7%
0.2985 29
4.8%
ValueCountFrequency (%)
0.5821 3
 
0.5%
0.5522 1
 
0.2%
0.5224 4
 
0.7%
0.4925 1
 
0.2%
0.4627 8
 
1.3%
0.4478 11
 
1.8%
0.4179 11
 
1.8%
0.3881 18
3.0%
0.3582 18
3.0%
0.3284 34
5.6%

casual
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5016393
Minimum0
Maximum47
Zeros172
Zeros (%)28.2%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-10-19T19:14:56.624629image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile17
Maximum47
Range47
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.319945
Coefficient of variation (CV)1.4039208
Kurtosis9.8806518
Mean4.5016393
Median Absolute Deviation (MAD)2
Skewness2.7128691
Sum2746
Variance39.941705
MonotonicityNot monotonic
2023-10-19T19:14:56.759712image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 172
28.2%
1 84
13.8%
3 57
 
9.3%
2 55
 
9.0%
4 38
 
6.2%
5 34
 
5.6%
6 30
 
4.9%
7 22
 
3.6%
8 18
 
3.0%
9 15
 
2.5%
Other values (22) 85
13.9%
ValueCountFrequency (%)
0 172
28.2%
1 84
13.8%
2 55
 
9.0%
3 57
 
9.3%
4 38
 
6.2%
5 34
 
5.6%
6 30
 
4.9%
7 22
 
3.6%
8 18
 
3.0%
9 15
 
2.5%
ValueCountFrequency (%)
47 1
 
0.2%
41 1
 
0.2%
40 1
 
0.2%
35 2
0.3%
33 1
 
0.2%
29 3
0.5%
26 2
0.3%
24 1
 
0.2%
23 3
0.5%
22 3
0.5%

registered
Real number (ℝ)

HIGH CORRELATION 

Distinct150
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.068852
Minimum0
Maximum247
Zeros3
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-10-19T19:14:56.910093image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q114
median43
Q370
95-th percentile155
Maximum247
Range247
Interquartile range (IQR)56

Descriptive statistics

Standard deviation47.021204
Coefficient of variation (CV)0.92074134
Kurtosis2.1650171
Mean51.068852
Median Absolute Deviation (MAD)28.5
Skewness1.4288749
Sum31152
Variance2210.9936
MonotonicityNot monotonic
2023-10-19T19:14:57.132089image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 34
 
5.6%
2 21
 
3.4%
3 17
 
2.8%
6 13
 
2.1%
5 13
 
2.1%
55 12
 
2.0%
54 10
 
1.6%
8 9
 
1.5%
31 9
 
1.5%
26 9
 
1.5%
Other values (140) 463
75.9%
ValueCountFrequency (%)
0 3
 
0.5%
1 34
5.6%
2 21
3.4%
3 17
2.8%
4 9
 
1.5%
5 13
 
2.1%
6 13
 
2.1%
7 7
 
1.1%
8 9
 
1.5%
9 7
 
1.1%
ValueCountFrequency (%)
247 1
0.2%
233 1
0.2%
218 1
0.2%
216 1
0.2%
214 1
0.2%
210 1
0.2%
208 1
0.2%
207 1
0.2%
202 1
0.2%
197 1
0.2%

cnt
Real number (ℝ)

HIGH CORRELATION 

Distinct165
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.570492
Minimum1
Maximum249
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-10-19T19:14:57.301271image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q116
median47
Q379.75
95-th percentile158.55
Maximum249
Range248
Interquartile range (IQR)63.75

Descriptive statistics

Standard deviation49.316802
Coefficient of variation (CV)0.88746383
Kurtosis1.5354409
Mean55.570492
Median Absolute Deviation (MAD)32
Skewness1.2435625
Sum33898
Variance2432.1469
MonotonicityNot monotonic
2023-10-19T19:14:57.526633image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 32
 
5.2%
2 20
 
3.3%
3 20
 
3.3%
5 15
 
2.5%
57 10
 
1.6%
59 10
 
1.6%
28 10
 
1.6%
36 9
 
1.5%
84 9
 
1.5%
6 9
 
1.5%
Other values (155) 466
76.4%
ValueCountFrequency (%)
1 32
5.2%
2 20
3.3%
3 20
3.3%
4 7
 
1.1%
5 15
2.5%
6 9
 
1.5%
7 8
 
1.3%
8 8
 
1.3%
9 6
 
1.0%
10 3
 
0.5%
ValueCountFrequency (%)
249 1
0.2%
238 1
0.2%
225 1
0.2%
222 1
0.2%
219 1
0.2%
217 2
0.3%
215 1
0.2%
212 1
0.2%
210 1
0.2%
202 1
0.2%

Interactions

2023-10-19T19:14:51.022542image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:39.117974image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:40.643334image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:41.951321image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:43.769390image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:45.205045image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:46.727754image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:48.194289image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:49.530166image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:51.166739image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:39.325541image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:40.779111image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:42.105438image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:43.921780image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:45.379239image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:46.887859image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:48.354484image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:49.706532image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:51.325222image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:39.478662image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:40.913087image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:42.268457image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:44.074349image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:45.544800image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:47.046765image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:48.494353image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:49.861814image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:51.459401image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:39.649399image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:41.035409image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:42.826349image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:44.202181image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:45.700426image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:47.194431image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:48.626518image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:50.020571image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:51.625039image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:39.820249image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:41.159448image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:42.976785image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:44.372404image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:45.873969image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:47.372030image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:48.773895image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:50.190850image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:51.815982image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:40.028130image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:41.341374image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:43.145748image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:44.565953image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:46.075252image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:47.544689image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:48.938722image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:50.373388image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:51.968916image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:40.198027image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:41.505492image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:43.298764image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:44.740006image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:46.255621image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:47.733143image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:49.076669image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:50.581815image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:52.119534image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:40.350772image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:41.636412image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:43.442197image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:44.884103image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:46.423699image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:47.882735image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:49.237129image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:50.728096image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:52.277471image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:40.499924image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:41.794459image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:43.612929image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:45.044052image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:46.578392image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:48.042806image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:49.386067image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-19T19:14:50.874489image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-10-19T19:14:57.710317image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
instanthrtempatemphumwindspeedcasualregisteredcntdtedayholidayweekdayweathersit
instant1.0000.025-0.221-0.2370.040-0.046-0.0360.0540.0320.9170.5950.4470.285
hr0.0251.0000.1600.114-0.2240.1280.3290.4590.4560.0000.0000.0000.000
temp-0.2210.1601.0000.9060.1350.0580.3970.2290.2660.4640.2310.2490.215
atemp-0.2370.1140.9061.0000.273-0.3060.3160.1660.2010.4340.1620.2560.192
hum0.040-0.2240.1350.2731.000-0.341-0.329-0.313-0.3140.3530.0840.2120.375
windspeed-0.0460.1280.058-0.306-0.3411.0000.1360.1000.1100.2470.1640.1730.128
casual-0.0360.3290.3970.316-0.3290.1361.0000.6020.6670.1450.0000.0970.000
registered0.0540.4590.2290.166-0.3130.1000.6021.0000.9930.0410.0000.0940.033
cnt0.0320.4560.2660.201-0.3140.1100.6670.9931.0000.0550.0000.0850.085
dteday0.9170.0000.4640.4340.3530.2470.1450.0410.0551.0000.9780.9820.448
holiday0.5950.0000.2310.1620.0840.1640.0000.0000.0000.9781.0000.4670.223
weekday0.4470.0000.2490.2560.2120.1730.0970.0940.0850.9820.4671.0000.157
weathersit0.2850.0000.2150.1920.3750.1280.0000.0330.0850.4480.2230.1571.000

Missing values

2023-10-19T19:14:52.519047image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-19T19:14:52.919710image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

instantdtedayseasonhrholidayweekdayweathersittempatemphumwindspeedcasualregisteredcnt
0101-01-2011Spring0FalseSaturday10.240.28790.810.000031316
1201-01-2011Spring1FalseSaturday10.220.27270.800.000083240
2301-01-2011Spring2FalseSaturday10.220.27270.800.000052732
3401-01-2011Spring3FalseSaturday10.240.28790.750.000031013
4501-01-2011Spring4FalseSaturday10.240.28790.750.0000011
5601-01-2011Spring5FalseSaturday20.240.25760.750.0896011
6701-01-2011Spring6FalseSaturday10.220.27270.800.0000202
7801-01-2011Spring7FalseSaturday10.200.25760.860.0000123
8901-01-2011Spring8FalseSaturday10.240.25760.750.0000178
91001-01-2011Spring9FalseSaturday10.320.34850.760.00008614
instantdtedayseasonhrholidayweekdayweathersittempatemphumwindspeedcasualregisteredcnt
60060128-01-2011Spring6FalseFriday20.180.19700.800.134301616
60160228-01-2011Spring7FalseFriday20.160.19700.860.089625860
60260328-01-2011Spring8FalseFriday20.160.19700.860.08962155157
60360428-01-2011Spring9FalseFriday30.180.21210.860.0896695101
60460528-01-2011Spring10FalseFriday30.180.21210.860.104504949
60560628-01-2011Spring11FalseFriday30.180.21210.930.104503030
60660728-01-2011Spring12FalseFriday30.180.21210.930.104512829
60760828-01-2011Spring13FalseFriday30.180.21210.930.104503131
60860928-01-2011Spring14FalseFriday30.220.27270.800.000023638
60961028-01-2011Spring15FalseFriday20.200.25760.860.000014041